Paper Abstract and Keywords |
Presentation |
2021-11-18 16:25
Memory Efficient Training of Neural ODE by Symplectic Adjoint Method Takashi Matsubara, Yuto Miyatake (Osaka Univ.), Takaharu Yaguchi (Kobe Univ.) CCS2021-23 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Neural ODE learns an ordinary differential equation using neural networks, thereby modeling a continuous-time dynamics and probabilistic model with high accuracy. However, it uses the same neural network repeatedly, the backpropagation algorithm for training consumes extraordinary memory. Hence, the adjoint method has been often used, which calculates a gradient by a numerical integration. It requires much computational cost to suppress the numerical errors. Sanz-Serna, 2016, proposed to use a symplectic integrator for the adjoint method, thereby obtaining the exact gradient. In this study, we combine this method with a checkpointing scheme, and balance the fast calculation and small memory requirements. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Neural ODE / ordinary differential equation / adjoint method / symplectic integrator / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 253, CCS2021-23, pp. 31-36, Nov. 2021. |
Paper # |
CCS2021-23 |
Date of Issue |
2021-11-11 (CCS) |
ISSN |
Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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CCS2021-23 |
Conference Information |
Committee |
CCS |
Conference Date |
2021-11-18 - 2021-11-19 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Osaka Univ. |
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(See Japanese page) |
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Paper Information |
Registration To |
CCS |
Conference Code |
2021-11-CCS |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Memory Efficient Training of Neural ODE by Symplectic Adjoint Method |
Sub Title (in English) |
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Neural ODE |
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ordinary differential equation |
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adjoint method |
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symplectic integrator |
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1st Author's Name |
Takashi Matsubara |
1st Author's Affiliation |
Osaka University (Osaka Univ.) |
2nd Author's Name |
Yuto Miyatake |
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Osaka University (Osaka Univ.) |
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Takaharu Yaguchi |
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Kobe University (Kobe Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-11-18 16:25:00 |
Presentation Time |
25 minutes |
Registration for |
CCS |
Paper # |
CCS2021-23 |
Volume (vol) |
vol.121 |
Number (no) |
no.253 |
Page |
pp.31-36 |
#Pages |
6 |
Date of Issue |
2021-11-11 (CCS) |
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